| Challenge: | Nugget Proposal Networks (NPNs) can solve word-trigger mismatch problem . word-wise event detection models suffer from word-tree mismatch because of multiple triggers . |
| Approach: | They propose a novel way to detect event triggers in a character-wise paradigm . they propose entire trigger nuggets centered at each character regardless of word boundaries . |
| Outcome: | The proposed model outperforms the state-of-the-art methods on two datasets. |
Similar Papers
Event Detection with Trigger-Aware Lattice Neural Network (D19-1)
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| Challenge: | Event detection is a key part of event extraction, but there are two issues with word-based models in languages without natural delimiters, such as Chinese. |
| Approach: | They propose a framework that can solve the problem of word- trigger mismatch . they also use an external knowledge base to model polysemous characters and words . |
| Outcome: | The proposed model outperforms state-of-the-art methods on two benchmark datasets and outperformed previous state- of-the art methods significantly. |
Event Detection with Neural Networks: A Rigorous Empirical Evaluation (D18-1)
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| Challenge: | Neural network models have been the most successful for event detection, but they ignore syntactic relationships in the text. |
| Approach: | They propose a GRU-based model that combines syntactic information along with temporal structure through an attention mechanism. |
| Outcome: | The proposed model is competitive with existing models on a ACE2005 dataset. |
Zero-Shot Event Detection Based on Ordered Contrastive Learning and Prompt-Based Prediction (2022.findings-naacl)
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| Challenge: | Existing zero-shot event detection methods do not work for unseen types . supervised methods require predefined event types or external tools . |
| Approach: | They propose a framework to detect events from unstructured text without annotating samples . they propose to use ordered contrastive learning and prompt-based prediction to identify trigger words . |
| Outcome: | The proposed model detects events more effectively and accurately than state-of-the-art methods. |
Event Detection without Triggers (N19-1)
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| Challenge: | Existing approaches to event detection require annotated triggers and event types in training data. |
| Approach: | They propose a framework that encodes the representation of a sentence based on target event types. |
| Outcome: | The proposed framework achieves competitive performances compared with state-of-the-art methods. |
Zero-shot Label-Aware Event Trigger and Argument Classification (2021.findings-acl)
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| Challenge: | Existing work on event extraction relies on labor-intensive annotation, ignoring semantic meaning of event types' labels. |
| Approach: | They propose a zero-shot event extraction approach that first identifies events with existing tools and then maps them to a given taxonomy of event types in a no-shot manner. |
| Outcome: | The proposed approach doubles the performance of previous approaches on a ACE-2005 dataset . it leverages label representations induced by pre-trained language models and maps events to the target types . |
Graph based Neural Networks for Event Factuality Prediction using Syntactic and Semantic Structures (P19-1)
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| Challenge: | Existing work on event factuality prediction (EFP) relies on syntactic and semantic information to identify important context words. |
| Approach: | They propose a graph-based neural network that integrates syntactic and semantic information more effectively. |
| Outcome: | The proposed model integrates syntactic and semantic information more effectively . it provides more meaningful information for downstream tasks than classification formulations . |
Sequence-to-Nuggets: Nested Entity Mention Detection via Anchor-Region Networks (P19-1)
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| Challenge: | Named entity recognition (NER) approaches restrict each word belonging to at most one entity mention. |
| Approach: | They propose to model and leverage the head-driven phrase structures of entity mentions to solve this problem. |
| Outcome: | The proposed architecture achieves state-of-the-art on three standard nested entity mention detection benchmarks. |
Word-level Commonsense Knowledge Selection for Event Detection (2024.lrec-main)
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| Challenge: | Event Detection (ED) is a task of automatically extracting multi-class trigger words . Xie and Tu, 2022, use a Context-specific Knowledge Selector to select commonsense knowledge of words based on living contexts . |
| Approach: | They use a Context-specific Knowledge Selector to select the exact commonsense knowledge of words from a large knowledge base. |
| Outcome: | The proposed approach achieves the F1-score of about 78.3% on the ACE-2005 dataset. |
Heterogeneous Graph Neural Networks to Predict What Happen Next (2020.coling-main)
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| Challenge: | Existing work on event representation cannot capture discontinuous event segments . Existing models cannot represent heterogeneous relations and discontinuous events . |
| Approach: | They propose a heterogeneous-event graph network to model missing events . they employ each unique word and individual event as nodes in the graph . |
| Outcome: | The proposed model outperforms baseline models on one-step and multi-step inference tasks. |
The Art of Prompting: Event Detection based on Type Specific Prompts (2023.acl-short)
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| Challenge: | Experimental results show that a well-defined and comprehensive description of event types can significantly improve event detection performance when the annotations are limited. |
| Approach: | They propose a unified framework to integrate event type specific prompts for supervised, few-shot and zero-shot event detection. |
| Outcome: | The proposed framework shows up to 22.2% gain over the prior state-of-the-art frameworks. |